Abstract
To effectively maintain and analyze a large amount of real-time sensor data, one often uses a filtering technique that reflects characteristics of original data well. This paper proposes a novel method for recommending the measurement noise for Kalman filtering, which is one of the most representative filtering techniques. Kalman filtering corrects inaccurate values of input sensor data, and its filtering performance varies depending on the input noise parameters. In particular, if the noise parameters determined based on the user’s experience are incorrect, the accuracy of Kalman filtering may be reduced significantly. Based on this observation, this paper addresses how to determine the measurement noise variance, a major input parameter of Kalman filtering, by analyzing past sensor data and how to use the estimated noise to improve the filtering accuracy. More specifically, to estimate the measurement noise variance, two analytical methods are proposed: one a transform-based method using a wavelet transform and the other a learning-based method using a denoising autoencoder. Experimental results show that the proposed methods estimated the measurement noise variance accurately and were superior to the experience-based method in the filtering accuracy.
Highlights
Recent developments in sensor-based technologies such as 5G communications and IoT (Internet of Things) have increased innovative applications such as smart factories, smart cities, and wearable devices [1,2,3]
This paper aims to improve the performance of Kalman filtering [6,7,8], which is widely used for noise reduction and correction of sensor data, for example, in the field of computer vision, signal processing, and robotics due to its nice optimality property under certain conditions
This paper proposes transform-based and learning-based methods for estimating the measurement noise variance based on input sensor data
Summary
Recent developments in sensor-based technologies such as 5G communications and IoT (Internet of Things) have increased innovative applications such as smart factories, smart cities, and wearable devices [1,2,3]. Since these smart applications generate a huge amount of sensor data, one needs an effective filtering process to extract meaningful information from those massive data [4,5]. In the prediction step, it estimates the value to be measured from the current sensor based on the past sensor data. In the update step, it refines the estimated value in the prediction step using the measured value at the present time, to obtain a value closer to the actual one
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